Project 2

Pokemon Vignette

The objective for this project is to provide valuble endpoints for user to query information related to be beloved show Pokemon

Required packages for running this vignette are: httr, dplyr, ggplot2, ggthemes, and tidyr,gridextra, plotly, heatmaply, psych

#We will choose an API that provides Pokemon data for our exploration. To accomplish this, we will use the Poke API, which offers comprehensive pokemon-related data.
knitr::opts_chunk$set(echo = TRUE)
# Define API base URL
base_url <- "https://pokeapi.co/api/v2/pokemon"

# Function to get Pokémon details
get_pokemon_details <- function(pokemon_name_or_id) {
  url <- paste(base_url, "/", pokemon_name_or_id, sep = "")
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

Now we have started our api integration, we will work in a couple of functions bringing relevant information relating to statistics of interest to pokemon fanatics.

# Define API base URL for abilities
base_url_abilities <- "https://pokeapi.co/api/v2/ability"

# Function to get ability details
get_pokemon_ability <- function(ability_name_or_id) {
  url <- paste(base_url_abilities, "/", ability_name_or_id, sep = "")
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

# Define API base URL for moves
base_url_moves <- "https://pokeapi.co/api/v2/move"

# Function to get a list of moves
get_pokemon_moves <- function() {
  url <- base_url_moves
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

# Define API base URL for types
base_url_types <- "https://pokeapi.co/api/v2/type"

# Function to get a list of Pokémon types
get_pokemon_types <- function() {
  url <- base_url_types
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

# Function to get statistics for Pokémon of a specific type
get_pokemon_type_stats <- function(pokemon_type) {
  url <- paste(base_url_types, "/", pokemon_type, sep = "")
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

# Define API base URL for habitats
base_url_habitats <- "https://pokeapi.co/api/v2/pokemon-habitat"

# Function to get habitat details
get_pokemon_habitat <- function(habitat_name_or_id) {
  url <- paste(base_url_habitats, "/", habitat_name_or_id, sep = "")
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

# Define API base URL for shapes
base_url_shapes <- "https://pokeapi.co/api/v2/pokemon-shape"

# Function to get shape details
get_pokemon_shape <- function(shape_name_or_id) {
  url <- paste(base_url_shapes, "/", shape_name_or_id, sep = "")
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

# Define API base URL for colors
base_url_colors <- "https://pokeapi.co/api/v2/pokemon-color"

# Function to get color details
get_pokemon_color <- function(color_name_or_id) {
  url <- paste(base_url_colors, "/", color_name_or_id, sep = "")
  response <- GET(url)
  data <- content(response, "parsed")
  return(data)
}

base_url_types <- "https://pokeapi.co/api/v2/type"
url <- base_url_types
response <- GET(url)
response

## Response [https://pokeapi.co/api/v2/type]
##   Date: 2023-10-11 20:51
##   Status: 200
##   Content-Type: application/json; charset=utf-8
##   Size: 1.26 kB

We have now the ability to query endpoints relating to pokemon habitats and physical attributes. We are able to provide users pertient details on their favorite pokemon, which is almost their own pokedex!

# Collect details about specific Pokémon
charizard_details <- get_pokemon_details("charizard")
pikachu_details <- get_pokemon_details("pikachu")

# Collect details about a specific ability
overgrow_ability <- get_pokemon_ability("overgrow")

# Collect a list of moves
moves_list <- get_pokemon_moves()

# Collect a list of Pokémon types
types_list <- get_pokemon_types()

# Collect details about a specific habitat
cave_habitat <- get_pokemon_habitat("cave")

# Collect details about a specific shape
ball_shape <- get_pokemon_shape("ball")

# Collect details about a specific color
red_color <- get_pokemon_color("red")

# Collect statistics for Fire-type Pokémon
fire_type_stats <- get_pokemon_type_stats("fire")

With EDA, I want to get an understanding of the physical characterstics of pokemon, and look at the experience thresholds they may have. We may see stratification between more common vs rarer pokemon.

library(httr)
library(jsonlite)

pokemon <- read.csv('pokemon.csv')

head(pokemon)

##   id identifier species_id height weight base_experience order is_default
## 1  1  bulbasaur          1      7     69              64     1          1
## 2  2    ivysaur          2     10    130             142     2          1
## 3  3   venusaur          3     20   1000             263     3          1
## 4  4 charmander          4      6     85              62     5          1
## 5  5 charmeleon          5     11    190             142     6          1
## 6  6  charizard          6     17    905             267     7          1

# Contingency tables for height, weight, exp
table1 <- table(pokemon$height, pokemon$weight)
print("Contingency Table for height and weight")

## [1] "Contingency Table for height and weight"

table2 <- table(pokemon$height, pokemon$base_experience)
print("Contingency Table for height and exp")

## [1] "Contingency Table for height and exp"

table3 <- table(pokemon$weight, pokemon$base_experience)
print("Contingency Table for weight and exp")

## [1] "Contingency Table for weight and exp"

#Numerical summary
# Load the psych package
library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

# Create a subset of the dataframe with the columns you want to analyze
subset_data <- pokemon[, c("height", "weight", "base_experience")]

# Generate complex numerical summaries for center and spread
df <- describe(subset_data)
df

##                 vars    n   mean      sd median trimmed    mad min   max range
## height             1 1292  20.49   54.37     10   11.06   7.41   1  1000   999
## weight             2 1292 975.75 1971.99    315  479.45 385.48   0 10000 10000
## base_experience    3 1126 161.25   85.14    162  155.19 114.16  36   635   599
##                 skew kurtosis    se
## height          9.15   118.65  1.51
## weight          3.52    12.35 54.86
## base_experience 0.61     0.43  2.54

#interesting numerical analysis. Note high kurtosis and skew for height variable
#Did not print in markdown due to large output. General skewness to data

#Having issues with live API - so will read from raw file stemming from function instead

# Load the required library
library(ggplot2)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

# 1. Create a Bar Plot for Base Experience
base_experience_plot <- ggplot(pokemon, aes(x = identifier, y = base_experience)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  labs(title = "Base Experience by Pokemon", x = "Pokemon", y = "Base Experience") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# 2. Create a Scatter Plot for Height vs. Weight
scatter_plot <- ggplot(pokemon, aes(x = height, y = weight)) +
  geom_point(aes(color = identifier)) +
  labs(title = "Height vs. Weight of Pokemon", x = "Height", y = "Weight") +
  theme(legend.position = "none")

# 3. Create a Histogram for Heights
height_histogram <- ggplot(pokemon, aes(x = height)) +
  geom_histogram(fill = "lightgreen", bins = 20) +
  labs(title = "Distribution of Pokemon Heights", x = "Height", y = "Count")

# 4. Create a Box Plot for Base Experience
base_experience_boxplot <- ggplot(pokemon, aes(y = base_experience)) +
  geom_boxplot(fill = "lightblue") +
  labs(title = "Box Plot of Base Experience", x = "", y = "Base Experience")

# 5. Create a Bar Plot for Species Counts
species_counts_plot <- ggplot(pokemon, aes(x = identifier)) +
  geom_bar(fill = "lightcoral") +
  labs(title = "Number of Pokemon Species", x = "Pokemon", y = "Count") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Display the plots
library(gridExtra)
grid.arrange(base_experience_plot, scatter_plot, height_histogram, base_experience_boxplot, species_counts_plot, ncol = 2)

## Warning: Removed 166 rows containing missing values (`position_stack()`).

## Warning: Removed 166 rows containing non-finite values (`stat_boxplot()`).

# As we can see most of our data is skewed to the right. In all buckets (height, weight, experience), we see usual lower values with a few upward values present. This could be due to legendary pokemon which have great proportions and experience

Awesome 3D plot

library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:httr':
## 
##     config

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# Create a 3D scatter plot using plotly
scatter3d <- plot_ly(pokemon, x = ~height, y = ~weight, z = ~base_experience, color = ~identifier, 
                     text = ~identifier, marker = list(size = 3)) %>%
  add_markers() %>%
  layout(scene = list(xaxis = list(title = "Height"), 
                      yaxis = list(title = "Weight"), 
                      zaxis = list(title = "Base Experience")))

# Display the 3D graph
scatter3d

## Warning: Ignoring 166 observations

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

#Plot reiterates previous takeaway, with few outliers on the high end due to legendary status.